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| Retrospektywna analiza przeżycia× | Analiza przeżycia× | |
|---|---|---|
| Dziedzina≠ | Epidemiologia | Statystyka w badaniach |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1970s–1980s (retrospective variant established) | 1958 |
| Twórca≠ | Kaplan & Meier (foundational estimator, 1958); Cox (regression model, 1972); retrospective application is a design variant documented since the 1970s | Edward L. Kaplan and Paul Meier |
| Typ≠ | Retrospective observational analytical study | Method |
| Źródło pierwotne≠ | Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press. ISBN: 978-1439856789 | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Inne nazwy≠ | historical survival study, retrospective time-to-event analysis, retrospective follow-up survival study, archival survival analysis | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Pokrewne≠ | 5 | 3 |
| Podsumowanie≠ | Retrospective survival analysis applies time-to-event statistical methods — most commonly the Kaplan-Meier estimator and Cox proportional hazards regression — to data collected from past records rather than through prospective follow-up. The researcher looks back at medical records, disease registries, or administrative databases to reconstruct each patient's journey from a defined starting point (e.g., diagnosis or surgery) to an outcome of interest (e.g., death, relapse, or hospital readmission), making it a cost-efficient approach for studying prognosis and risk factors when prospective follow-up is not feasible. | Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters. |
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